最小均方算法在不同步长参数、不同滤波阶数和迭代下的性能分析

R. Nagal, Pradeep Kumar, Poonam Bansal
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引用次数: 6

摘要

针对不同的滤波阶数和迭代次数,通过改变LMS的步长参数μ,分析了LMS自适应消噪算法的性能。本文的工作在MATLAB中进行了仿真,验证了步长参数对最小均方算法的实现起着至关重要的作用。增大步长参数μ可以提高最小均方算法的收敛速度和稳定性。另一方面,当步长参数μ较小时,误差减小到很大,但算法收敛缓慢,趋于稳定。根据得到的结果,我们可以得出步长参数μ与收敛速度和误差减小成正比,与稳定性成反比的结论。这里介绍的工作还显示了实际权重和估计权重的比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance analysis of least mean square algorithm for different step size parameters with different filter order and iterations
This paper presents the performance analysis of Least Mean Square (LMS) algorithm for adaptive noise cancellation by varying its step size parameter μ for different filter order and no of iteration. The presented work has been simulated in MATLAB and verified that the step size parameter plays a vital role for implementation of Least Mean Square (LMS) algorithm. Increasing the step size parameter μ leads to fast convergence rate and instability of the least mean square algorithm. On the other side if the step size parameter μ is small then the error reduced to great amount but algorithm converges slowly and becomes stable. On the basis of obtained results we can conclude that step size parameter μ is directly proportional to convergence rate and error reduction and inversely proportional to stability. The work presented here also shown the comparison of actual weights and the estimated weights.
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